def update_map_constructeurs(input_value):
"""
Change la plage de données à afficher sur la carte en fonction des années cochées
Args:
Series "year"[]
"""
proddf = france[france.an_installation == 67]
year_data = france[france.an_installation == 66]
if input_value != []:
for year in input_value:
year_data = france[france.an_installation == year]
proddf = pd.concat([proddf, year_data], ignore_index=True)
else:
proddf = france
map_constructeurs = px.scatter_geo(proddf,
lon="lon",
lat="lat",
scope='europe',
size_max=15,
center=center_lat_lon,
color="panneaux_marque",
projection="natural earth")
map_constructeurs.update_layout(transition_duration=500,
geo=dict(projection_scale=5))
return map_constructeurs
def plot_map(loc_dataframe: pd.DataFrame):
"""Produce map and plots with meta-data on each potential job"""
figure = px.scatter_geo(loc_dataframe,
lat="latitude",
lon="longitude",
hover_name='title',
text='id')
figure.update_layout(mapbox_style="carto-darkmatter")
figure.show()
def plot_confirmed(tsmap_corona):
fig = px.scatter_geo(data_frame=tsmap_corona,
lat='Lat', lon='Long',
hover_name='Country/Region',
hover_data=['Province/State', 'Count'],
size='size',
animation_frame='Date',
size_max=40,
width=700, )
return fig
def create_graph(selected_value):
if selected_value == 'Above Benchmark':
df = psi90_df.query('Rate > 1')
else:
df = psi90_df.query('Rate < 1')
return px.scatter_geo(df,
lat='Latitude',
lon='Longitude',
locations='State',
locationmode='USA-states',
color='Provider_Name',
hover_name='Provider_Name',
scope='usa')
def update_map_production(input_value):
"""
Change la plage de données à afficher sur la carte en fonction de la surface sélectionnée
Args:
input_value[0] min
input_value[1] max
"""
imax = np.exp(input_value[1])
imin = np.exp(input_value[0])
proddf = france[france.surface <= imax]
proddf = proddf[proddf.surface >= imin]
map_production = px.scatter_geo(proddf,
lon="lon",
lat="lat",
scope='europe',
size_max=10,
center=center_lat_lon,
color="production_surface",
size="surface",
projection="natural earth")
map_production.update_layout(transition_duration=500,
geo=dict(projection_scale=5))
return map_production
px.line(gapminder, x="year", y="lifeExp", color="continent",
line_group="country", hover_name="country",
line_shape="spline", render_mode="svg")
#长条图
a=px.strip(data, x="x", y="y", orientation="h", color="name")
a.write_html('6.html')
#箱型图
a=px.box(data, x="x", y="y", color="name", notched=True)
a.write_html('7.html')
#小提琴图
a=px.violin(data, y="y", x="x", color="name", box=True, points="all",
hover_data=data.columns)
a.write_html('8.html')
'''
#地图
a = px.scatter_geo(gapminder,
locations="iso_alpha",
color="continent",
hover_name="country",
size="pop",
animation_frame="year",
projection="natural earth")
a.write_html('9.html')
re.sub(",",
"",
MM["Location Coordinates"][i].split()[0],
count=1)))
Lon.append(float(MM["Location Coordinates"][i].split()[1]))
else:
Lat.append(float("nan"))
Lon.append(float("nan"))
pass
#Add columns to original dataframe
MM["Lat"] = Lat
MM["Lon"] = Lon
#There is only one nan value, so we will delete it
sum(np.isnan(MM["Lat"]), 0)
MM = MM[~np.isnan(MM["Lat"])]
#Plot based on coordinates
GOLatLon = go.Figure()
GOLatLon = GOLatLon.add_trace(
go.Scattergeo(lon=MM["Lon"], lat=MM["Lat"], mode="markers"))
GOLatLon.show()
#Use size of dead and missing as marker size and as color as well
GOLatLonSize = px.scatter_geo(data_frame=MM,
lat="Lat",
lon="Lon",
size="Total Dead and Missing",
color="Total Dead and Missing")
GOLatLonSize.show()